What are flash crashes?

What are flash crashes?

A so-called Flash Crash is a sudden crash or fall in stock prices. Flash crashes are the sources of increasing concern among financial market practitioners and regulators because pinpointing their causes is difficult.

 One theory lays some or even much blame at the feet of high-speed electronic traders, arguing that the space previously occupied by bank traders has been colonized by algorithmic traders. These traders use computers to execute trades automatically and at high speed. But their defining feature is their slim levels of capital and their ability to change their trading strategies.

In quiet times, algorithmic traders are contrarians: buying when the market is falling and selling when the market is rising. Their behavior adds to liquidity. When the market starts to trend, these “algos” often switch into trading faster than others or more aggressively in the same direction of the trend. Selling more or before others drains liquidity and can create a flash crash.

What causes flash crashes?

As noted, flash crashes happen extremely quickly. It is clear that the drop and rebound in price are not because of the release of bad news and then good news immediately after. Another point to consider is that the crash can impact an entire stock index, not just one stock, bond, or commodity. The cause of the crash is not usually due to any perceived change in the fundamental value of the stock.

One cause of flash crashes stems from computer programs or algorithms. Using algorithms to trade has become increasingly popular. Computers can take large amounts of data and make large volume trades within a second, outside of human capabilities of reasoned decision making. Some algorithms are programmed to react to selling pressures.

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For example, as more sells are made, compared to buys, the algorithms start selling, too. This creates a snowball effect and at the speed with which computers make trades, a sudden plunge in the market occurs.

High-frequency traders are another factor that is believed to have caused or worsen flash crashes. These traders use algorithms to perform large transactions at very high speeds. Traders can also purposely use algorithms to partake in illegal trades such as spoofing. With spoofing, algorithms are programmed to put in fake sales.

 For example, the traders will put in an order to sell 400 sales, but pull the order right before it is satisfied. By simply placing such a large sell order, prices are driven down, and the traders will then buy at the reduced price.  With algorithms, this process has become very effective and is now illegal. This is just one way in which algorithms can create volatility in markets.

How can flash crashes be prevented?

Flash crashes are a phenomenon that is not fully understood. While it is clear human error can create the required spark, it is the computerized systems increasingly used to trade securities that ignite flash crashes. One of the characteristics of a flash crash is that there is a sharp price movement when there is no fundamental reason for such extreme volatility. Plus, the near-lightspeed at which they can happen shows the crash, and often the subsequent recovery, is driven by high frequency traders using algorithms.

It also seems clear that the lack of human participation, when major markets are closed and liquidity is low, increases the role of algorithmic traders. The fact most of these computers trade with one another (and themselves) means one fat finger or incorrect bit of programming of one algorithm often triggers another algorithm, which triggers another and so on.

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But the lack of true understanding about flash crashes means we are far from finding a solution that eradicates them altogether, demonstrated by the fact they keep happening regardless of what measures have been introduced by exchanges and others.

The reaction from the CME Group following the flash crash in silver futures suggest two interesting points. Firstly, you need safeguard algorithms to counter trading algorithms, meaning more computers to manage the computers that trade. Secondly, if the systems did their job but the flash crash still occurred then it shows safeguards put in place are about reacting to (and minimising the damage caused by) a flash crash rather than preventing them.

 One of the most popular measures introduced by exchanges such as the NYSE are circuit breakers, which halt trading when automated systems recognise a flash crash is occurring until buy and sell orders can be evenly matched up and trading can resume as normal.

The problem boils down to market structure. The dynamic of trading between two humans is vastly different to the dynamic of trade between two computer systems, with the former driven by emotion and sentiment and only capable of running for so many hours in the day, and the latter driven by technical forces and able to operate so long as a market is open.

 Human error often lays the ground work for a flash crash but it is computers that make it happen, implying a flaw in the relationship between human-computer trading. And yet, it is only humans that pay the price.

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Examples of flash crashes

Flash crashes occur frequently. However, most are very small and do not make the news. The first notable flash crash was the 2010 Flash Crash. During this crash, the Dow Jones index lost almost 9% of its value and around $1 trillion in equity in a very short space of time. However, the index was able to recover 70% of its decline by the end of the trading day.

This flash crash was especially significant for several reasons. First, it was especially notable due to the large plunge in value and was reported by many news outlets. Second, it brought attention to the role that computer algorithms can play in creating market volatility and uncertainty.

2019 Yen and Australian dollar

At the beginning of 2019, the Yen suddenly increased in relative value by 7% against the Australian dollar. The increase happened within minutes without any notice. This could have occurred due to a variety of reasons, including lower than normal liquidity and fear of global slowdown. One contributing factor was that on the morning of the crash, Apple stated that its profits were down due to a slowdown in China. The news drove investors to buy the yen, which is considered a safe-haven currency.